Performance of a Predictive Model versus Prescription-Based Thresholds in Identifying Patients at Risk of Fatal Opioid Overdose

Abstract

Background: Prescription Drug Monitoring Programs (PDMPs) collect controlled substance prescriptions dispensed within a state. Many PDMP programs perform targeted outreach (i.e., “unsolicited reporting”) for patients who exceed numerical thresholds, however, the degree to which patients at highest risk of fatal opioid overdose are identified has not been compared with one another or with a predictive model. Methods: A retrospective analysis was performed using statewide PDMP data for Maryland residents aged 18 to 80 years with an opioid fill between April to June 2015. The outcome was opioid-related overdose death in 2015 or 2016. A multivariable logistic regression model and three PDMP thresholds were evaluated: (1) multiple provider episodes; (2) high daily average morphine milligram equivalents (MME); and (3) overlapping opioid and benzodiazepine prescriptions. Results: The validation cohort consisted of 170,433 individuals and 244 deaths. The predictive model captured more individuals who died (46.3% of total deaths) and had a higher death rate (7.12 per 1000) when the risk score cutoff (0.0030) was selected for a comparable size of high-risk individuals (n = 15,881) than those meeting the overlapping opioid/benzodiazepine prescriptions (n = 17,440; 33.2% of total deaths; 4.64 deaths per 1000) and high MME (n = 14,675; 24.6% of total deaths; 4.09 deaths per 1000) thresholds. Conclusions: The predictive model identified more individuals at risk of fatal opioid overdose as compared with PDMP thresholds commonly used for unsolicited reporting. PDMP programs could improve their targeting of unsolicited reports to reach more individuals at risk of overdose by using predictive models instead of simple threshold-based approaches.